CompTIA SecAI+ Certification Training (Instructor-Led Online)

CompTIA SecAI+ (Instructor-Led) From 9am to 4pm EDT (US and Canada) Learn how to secure, govern and responsibly integrate artificial intelligence into your cybersecurity operations. This training provides the most comprehensive approach to earning the CompTIA SecAI+ certification — one of the most relevant new credentials for today’s AI-enabled security landscape. Prepare for your SecAI+ (V1) certification with access to the SecAI+ Complete Learning Bundle with live online instructor-led sessions, voucher Plus retake assurance. This complete learning solution equips you with the knowledge and exam prep tools needed to master SecAI+ (V1) concepts and pass your certification exam. What's included: Live online instructor-led sessions Voucher Plus Retake Assurance CertMaster Perform Key benefits: Comprehensive preparation: Master SecAI+ objectives with lessons and practice tools focused on AI concepts, securing AI systems, AI-assisted security, and AI governance, risk, and compliance. Interactive learning: Engage with videos, quizzes, and lab activities mapped to real-world AI security and cybersecurity tasks. Extensive practice: Access hundreds of questions, assessments, and timed practice tests to reinforce your knowledge. Gradable assessments: Track your progress with actionable feedback from practice tools. Exam attempt with retake assurance: Take your exam confidently, knowing you’re covered to retake it if needed. Year-long access: Redeem your product through CompTIA Central within 12 months of your access codes being made available, then enjoy 12 months of training access from the redemption date. Easy voucher redemption: Redeem and schedule your exam at CompTIA Central. Skills you'll learn Build skills with CompTIA learning and validate them with SecAI+ certification. Apply AI concepts to strengthen your organization’s cybersecurity posture. Secure AI systems using advanced controls and protections to safeguard data, models, and infrastructure. Leverage AI technologies to automate workflows, accelerate incident response, and scale security operations. Navigate global GRC frameworks to ensure ethical and compliant AI adoption across industries. Defend against AI-driven threats like adversarial attacks, automated malware, and malicious use of generative AI. Integrate AI securely into DevSecOps pipelines and enterprise security strategies. Training Outline Learning Objectives Module 1 — AI and Data Concepts for Cybersecurity AI concepts and core AI types Generative AI and transformers Machine learning and deep learning Natural language processing AI model training approaches Prompt engineering fundamentals Model security considerations AI data types and data security techniques RAG (Retrieval Augmented Generation) concepts Data integrity and processing controls Module 2 — Threat Modeling and Securing AI Systems AI threat modeling fundamentals Threat modeling processes and prerequisites AI threat modeling frameworks AI security control types Model guardrails and prompt templates Gateway and interface controls Usage quotas and limitation controls Security control testing Module 3 — Access Controls for AI AI access control principles and models Model and agent access controls API and network access security AI data security controls Encryption and data safety measures Monitoring and logging AI systems Performance and cost monitoring AI auditing and compliance monitoring Module 4 — AI Threats and Compensating Controls AI lifecycle security Ethical AI design considerations AI attack types and techniques Backdoor and trojan model attacks Model poisoning and inversion Model theft risks Compensating control strategies Post-incident AI analysis Module 5 — Leveraging AI in Security Operations AI-enabled security tools AI use cases in detection and analysis AI for vulnerability assessment AI-enhanced attack vectors AI for social engineering and deception AI reconnaissance techniques AI-driven automation AI in DevSecOps workflows AI scripting and summarisation Module 6 — AI Governance, Risk, and Compliance AI governance structures AI organisational roles Responsible AI principles AI risk identification and assessment AI regulatory themes Compliance frameworks for AI Organisational AI policy design Compliance reporting Exam series code: CY0-001 Exam details Exam version: V1 Exam series code: CY0-001 Launch date: February 17, 2026 Number of questions: Maximum of 60, multiple-choice and performance-based Duration: 60 minutes Passing score: 600 (on a scale of 100–900) Languages: English Recommended experience: 3–4 years in IT and 2+ years hands-on cybersecurity; Security+, CySA+, PenTest+, or equivalent recommended Retirement: Estimated 3 years after launch SecAI+ (V1) exam objectives Basic AI concepts related to cybersecurity (17%) Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation. Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations. Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI. Securing AI systems (40%) Implement security controls: Protect AI systems, data, and models using robust technical safeguards. Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures. Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers. AI-assisted security (24%) Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation. Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration. Apply AI techniques in operations: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring. AI governance, risk, and compliance (19%) Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption. Integrate GRC into AI projects: Incorporate governance, risk management, and compliance practices throughout the AI lifecycle. Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF Request More Information